130 Kneuron, 130 Msynaps self-learning processor

October 2, 2017 | 00:00

130 Kneuron, 130 Msynaps self-learning processor. Image: Intel.

Machine learning models based on CNNs use large training sets to set up recognition of objects and events. However, unless their training sets have specifically accounted for a particular element, situation or circumstance, these machine learning systems do not generalize well.
Intel's ‘Loihi’ neuromorphic chip mimics how the brain functions by learning to operate based on various modes of feedback from the environment. Such a system does not need to be trained in the traditional way and can improve its performance over time.

Unlike convolutional neural network (CNN) and other deep learning processors, Intel’s newest ‘Loihi’ chip now under development uses an asynchronous spiking model to mimic neuron and synapse behavior in a much closer analog to animal brain behaviour. This is similar to the work of startup BrainChip Inc although for now BrainChip is offering a solution based on an FPGA implementation (see BrainChip launches neuromorphic hardware accelerator ).

Machine learning models based on CNNs use large training sets to set up recognition of objects and events. However, unless their training sets have specifically accounted for a particular element, situation or circumstance, these machine learning systems do not generalize well.
Intel's ‘Loihi’ neuromorphic chip mimics how the brain functions by learning to operate based on various modes of feedback from the environment. Such a system does not need to be trained in the traditional way and can improve its performance over time.

The new chip is manufactured using Intel’s 14-nm FinFET process. It is claimed to be capable of representing 130,000 neurons and 130 million synapses.

Intel said it would be sharing the Loihi test chip with leading university and research institutions with a focus on advancing AI in the first half of 2018.